spectrumlab 0.1.2

A pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy.

A pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy.

🚀 Quick Start

Environment Setup

We recommend using conda and uv for environment management:

# Clone the repository
git clone https://github.com/little1d/SpectrumLab.git
cd SpectrumLab

# Create conda environment
conda create -n spectrumlab python=3.10
conda activate spectrumlab

pip install uv
uv pip install -e .

Data Setup

Download benchmark data from Hugging Face:

• SpectrumBench v1.0

Extract the data to the data directory in the project root.

API Keys Configuration

# Copy and edit environment configuration
cp .env.example .env
# Configure your API keys in the .env file

💻 Usage

Python API

from spectrumlab.benchmark import get_benchmark_group
from spectrumlab.models import GPT4o
from spectrumlab.evaluator import get_evaluator

# Load benchmark data
benchmark = get_benchmark_group("perception")
data = benchmark.get_data_by_subcategories("all")

# Initialize model
model = GPT4o()

# Get evaluator
evaluator = get_evaluator("perception")

# Run evaluation
results = evaluator.evaluate(
    data_items=data,
    model=model,
    save_path="./results"
)

print(f"Overall accuracy: {results['metrics']['overall']['accuracy']:.2f}%")

Command Line Interface

The CLI provides a simple way to run evaluations:

# Basic evaluation
spectrumlab eval --model gpt4o --level perception

# Specify data path and output directory
spectrumlab eval --model claude --level signal --data-path ./data --output ./my_results

# Evaluate specific subcategories
spectrumlab eval --model deepseek --level semantic --subcategories "IR_spectroscopy" "Raman_spectroscopy"

# Customize output length
spectrumlab eval --model internvl --level generation --max-length 1024

# Get help
spectrumlab eval --help

🤝 Contributing

We welcome community contributions! Please see CONTRIBUTING.md for detailed guidelines.

Acknowledgments

• Experiment Tracking: SwanLab for experiment management and visualization
• Choice Evaluator Framework: Inspired by MMAR